An information security management and monitoring system based on big data

Abstract

The invention discloses an information security management and monitoring system based on big data, which relates to the field of information security and includes an information management center, wherein an information collection module, an information processing module, a data monitoring module, a monitoring and early warning module, an information encryption module and an information storage module are communicatively connected; the information collection module is used to collect resource data information in an enterprise account; the information processing module is used to perform reliability analysis on the resource data information and classify and mark the analysis results; the data detection module is used to perform simulation tests on the resource data information according to the classification marks to obtain simulated monitoring data; the monitoring and early warning module is used to judge whether the simulated monitoring data is qualified according to the simulated monitoring data; the information encryption module is used to perform encryption processing on qualified resource data information; the information storage module is used to store the resource data information that has completed the encryption processing; the invention improves the security of enterprise information.

Description

Technical Field

[0001] The present invention relates to the field of information security, and in particular to an information security management and monitoring system based on big data. Background Art

[0002] With the rapid development of information technology, in today's information society, a large amount of personal information and key data are widely collected, stored and transmitted; in order to ensure the confidentiality, integrity and availability of these data, information security protection has become crucial; however, in the face of growing network security threats, such as viruses, malware, hacker attacks, etc., traditional security defense methods can no longer meet the needs of modern business; therefore, it is particularly important to develop a solution that can effectively use big data technology for intelligent security protection;

[0003] A security management method and system based on enterprise software vulnerability identification, with publication number CN116401679B, discloses a security management method and system based on enterprise software vulnerability identification, which relates to the field of data processing, wherein the method includes: obtaining a vulnerability monitoring operator based on a vulnerability monitoring operator analysis model and target software vulnerability monitoring characteristics; controlling a vulnerability monitoring platform based on the vulnerability monitoring operator to perform real-time vulnerability monitoring on the target enterprise software to obtain a real-time vulnerability monitoring data group; obtaining M vulnerability feature information; performing operation and maintenance analysis on the M vulnerability feature information based on a vulnerability operation and maintenance management model to obtain a vulnerability operation and maintenance management plan, and performing security management on the target enterprise software based on the plan. The method solves the technical problem that the accuracy of vulnerability security management for enterprise software in the prior art is insufficient, which in turn causes the low quality of vulnerability security management for enterprise software. The method achieves the technical effect of improving the accuracy of vulnerability security management for enterprise software and improving the quality of vulnerability security management for enterprise software.

[0004] At present, information security management and monitoring mainly focus on static protection and passive protection, which are based on the original rule experience and passively waiting for security incidents to occur to take emergency response, so as to achieve information security management. However, how to dynamically and actively protect information security is a problem we need to solve. To this end, this paper provides an information security management and monitoring system based on big data. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide an information security management and monitoring system based on big data.

[0006] The object of the present invention can be achieved by the following technical solutions: an information security management and monitoring system based on big data, comprising an information management center, wherein the information management center is communicatively connected with an information collection module, an information processing module, a data monitoring module, a monitoring and early warning module, an information encryption module and an information storage module;

[0007] The information collection module is used to collect user data information and resource data information in the corresponding enterprise account, and mark the data source of the obtained resource data information;

[0008] The information processing module is used to perform reliability analysis on the resource data information according to its data source, and classify and mark the obtained resource data information according to the reliability analysis result;

[0009] The data monitoring module is used to perform simulation tests on resource data information according to their classification marks, and obtain simulation monitoring data according to the simulation test results;

[0010] The monitoring and early warning module is used to verify the reliability of the corresponding resource data information according to the simulated monitoring data, determine whether the resource data information is qualified, and perform early warning processing on the data information according to the judgment result;

[0011] The information encryption module is used to encrypt resource data information with qualified reliability according to the classification results of the reliability analysis corresponding to the data information, and send the resource data information after the encryption processing to the information storage module;

[0012] The information storage module is used to store the resource data information that has completed encryption processing according to the enterprise account.

[0013] Furthermore, the process of the information collection module collecting user data information and resource data information in the corresponding enterprise account includes:

[0014] The information collection module is used for user accounts within the enterprise account to enter resource data information. Before entering the resource data information, the user data information of the corresponding user account within the enterprise account is entered, the obtained user data information is associated with the entered resource data information, and the obtained data information is marked according to the association result and sent to the information processing module.

[0015] Furthermore, the process of the information processing module performing reliability analysis on the data information according to its data source includes:

[0016] The obtained user data information is used as the data source of the resource data information, the user data information is analyzed and processed, the historical user data information in the corresponding enterprise account stored in the information storage module is obtained, the obtained user data information and the historical user data information are matched with each other, if the match is successful, it is marked as a historical account, if the match is not successful, it is marked as an emerging account; the user activity cycle is preset, the user data information corresponding to the successfully matched historical account and the historical user data information are mapped to the user activity cycle, the activity rate of the user account is obtained, and the activity level of the corresponding user account is obtained according to the activity rate, and the activity level includes a low activity level, a normal activity level and a high activity level;

[0017] Obtain corresponding resource data information, perform feature analysis on the resource data information, and obtain resource feature data of the corresponding resource data information based on the feature analysis. The information processing module is provided with resource representation feature data based on the historical resource data information in the corresponding enterprise account. Perform similarity analysis on the obtained resource feature data and the resource representation feature data to obtain corresponding similarity data.

[0018] Furthermore, the process of the information processing module classifying and marking the obtained data information according to the reliability analysis result includes:

[0019] The information processing module sets corresponding weight factors according to the marking results of the user account and the similarity data of the resource data information, analyzes the marking results and the similarity data according to the weight factors, and obtains their reliability data; presets the reliability classification interval, compares and analyzes the obtained reliability data with the reliability classification interval, and obtains its classification results, wherein the classification results include three types: high reliability, medium reliability and low reliability.

[0020] Furthermore, the data monitoring module performs simulation tests on the resource data information according to the classification tags thereof, and the process of obtaining simulation monitoring data according to the simulation test results includes:

[0021] The data monitoring module obtains resource data information classified as high reliability, medium reliability and low reliability, and sets corresponding simulation monitoring cycles according to the reliability levels;

[0022] Obtain historical resource data information in the corresponding enterprise account, scramble the historical resource data information and extract lost values ​​to obtain lost resource data information, construct a virtual disguised enterprise model based on the obtained lost resource data information based on a machine learning algorithm, input the obtained resource data information into the virtual disguised enterprise model, and obtain simulated monitoring data in the virtual disguised enterprise model corresponding to the resource data information in the corresponding simulated monitoring period;

[0023] The simulated monitoring data corresponding to the corresponding resource data information within its corresponding simulated monitoring period is sent to the monitoring and early warning module.

[0024] Furthermore, the monitoring and early warning module verifies the reliability of the corresponding resource data information according to the simulated monitoring data, and the process of judging whether the resource data information is qualified includes:

[0025] The monitoring and early warning module obtains the simulated monitoring data corresponding to the resource data information within the corresponding simulated monitoring period, performs risk assessment on the obtained simulated monitoring data, and obtains abnormal data information corresponding to the simulated monitoring data, wherein the abnormal data information includes but is not limited to outlier abnormal data, associated abnormal data and missing value abnormal data;

[0026] The monitoring and early warning module analyzes and processes the abnormal data information of the simulated detection data within the simulated detection cycle, obtains the abnormal unit time of the abnormal data information within the corresponding simulated detection cycle, superimposes the abnormal unit time corresponding to different types of abnormal data information, obtains the abnormal risk data of the resource data information, and compares and analyzes the obtained abnormal risk data with the corresponding abnormal risk threshold according to different preset abnormal risk thresholds of the simulated detection cycle. The reliability of the corresponding resource data information is judged according to the comparative analysis results. When the abnormal risk data is greater than or equal to the corresponding abnormal risk threshold, the resource data information is unqualified and resource early warning information is generated; when the abnormal risk data is less than the corresponding abnormal risk threshold, the resource data information is qualified and sent to the information encryption module.

[0027] Furthermore, the process of the information encryption module encrypting the data information according to the classification result of the reliability analysis corresponding to the data information includes:

[0028] The information encryption module obtains qualified resource data information and user data information associated therewith, and sets corresponding key information according to the obtained user data information and resource data information;

[0029] Setting corresponding encoding strings for the user data information and resource data information obtained in the corresponding enterprise account, cross-scrambling the encoding strings corresponding to the corresponding user data information and resource data information according to the number of bits they are in, and obtaining scrambled encoding strings;

[0030] The information encryption module is provided with a coding number extraction algorithm, based on which corresponding characters in a scrambled coding string are obtained. After one extraction is completed, the scrambled coding string is digitally rearranged to obtain a new scrambled coding string, and parameters of the coding number extraction algorithm are adjusted according to the extracted characters. Based on the coding number extraction algorithm after the parameter adjustment, corresponding characters in the new scrambled coding string are obtained, and the operation is repeated until characters of corresponding digits are extracted, and the obtained characters are set as key information, and corresponding resource data information is encrypted according to the obtained key information, and the encrypted resource data information is sent to the information storage module.

[0031] Furthermore, the information storage module generates a corresponding resource storage space according to the corresponding enterprise account, marks the resource storage space according to the corresponding enterprise account, and stores the encrypted resource data information in the corresponding enterprise account and its corresponding user data information in the corresponding resource storage space.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. Acquire the user data information of the corresponding user account while acquiring the resource data information, analyze and process the acquired resource data information according to the user data information, acquire the reliability data of the corresponding resource data information, acquire the reliability level of the corresponding resource data information according to the reliability data, input it into the virtual disguised enterprise model corresponding to the corresponding enterprise account according to the reliability level, acquire the corresponding simulated monitoring data, judge whether the corresponding resource data information has a threat according to the simulated monitoring data, and verify and process each resource data information, thereby improving the security of the resource data information in the corresponding enterprise account to a certain extent;

[0034] 2. By setting up a virtual disguised enterprise model, simulate the test of the resource data information entered in the enterprise account, so as to dynamically and actively protect the resource data information in the enterprise account, avoid the impact of the entered resource data information on other resource data information in the enterprise account, test it in advance, and thus dynamically and actively protect the information security;

[0035] 3. By encrypting the verified resource data information, setting the corresponding key information according to the scrambled coding string, and encrypting the resource data information according to the set key information, it is avoided that the encryption algorithm sets a unified key information, which causes a large amount of resource data information in the enterprise account to be leaked, thereby ensuring the independence of resource data information to a certain extent while improving its security. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1This is a schematic diagram of an information security management and monitoring system based on big data according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] like Figure 1 As shown, an information security management and monitoring system based on big data includes an information management center, and the information management center is communicatively connected with an information collection module, an information processing module, an information encryption module, an information storage module, a data monitoring module and a monitoring and early warning module;

[0038] The information management center user obtains the corresponding enterprise registration information, which includes enterprise registration qualification information, legal representative information, legality certification information and operation status information, verifies and analyzes the obtained enterprise registration information, and determines whether the corresponding enterprise grants it the right to enter the information management center. If the verification is successful, the corresponding enterprise is granted the right to enter the information management center and the corresponding enterprise account is granted. If the verification is unsuccessful, the corresponding enterprise is not granted the right to enter the information management center.

[0039] The information management center sets up a corresponding enterprise management space based on the successfully verified enterprise account, and stores relevant data information in the enterprise account in the enterprise management space. The enterprise management space is associated with the corresponding module, and the corresponding module completes data monitoring and management based on the association result.

[0040] The information collection module is used to collect the corresponding data information in the corresponding enterprise account, obtain the corresponding user data information and resource data information, and mark the obtained data information. The specific implementation process includes:

[0041] An information input unit is provided in the information collection module;

[0042] The information entry unit is used for entering resource data information and user data information of the corresponding enterprise account into the corresponding user account. Before entering the corresponding resource data information, the corresponding user data information is entered. The user data information includes the user identity information, position information, entry record information and enterprise authentication information of the corresponding user account. The resource data information includes but is not limited to financial resource data, customer resource data, product data information, supply product data, business-related data and log resource data; the obtained resource data information is associated with the corresponding user account and sent to the information processing module;

[0043] It should be further explained that, in the specific implementation process, the purpose of obtaining the corresponding user data information in the information collection module is to ensure the security of the resource data information entered into the enterprise account, and to analyze and process the obtained resource data information based on the corresponding user data information, thereby improving the security of the resource data information in the enterprise account.

[0044] The information processing module is used to perform reliability analysis on the data information according to its source, and classify and mark the obtained data information according to the reliability analysis result. The specific implementation process includes:

[0045] The information processing module is provided with an information verification unit and a reliability analysis unit;

[0046] The information verification unit is used to verify the obtained user data information; the information storage module stores historical user data information of the corresponding user account in the corresponding enterprise account; the obtained user data information is used as the data source of the resource data information, the user data information is analyzed and processed, the historical user data information in the corresponding enterprise account stored in the information storage module is obtained, the obtained user data information is matched with the historical user data information, if the match is successful, it is marked as a historical account, if the match is not successful, it is marked as a new account;

[0047] Preset a user activity cycle, map the user data information corresponding to the successfully matched historical account and the historical user data information into the user activity cycle, and obtain the activity rate of the user account, where the activity rate is the proportion of the mapping results of the user data information of the corresponding user account per unit time in the user activity cycle; obtain the activity level of the corresponding user account according to the activity rate, where the activity level includes a low activity level, a normal activity level, and a high activity level;

[0048] Preset an active ratio level interval, compare and analyze the obtained active rate and active ratio level interval, obtain the active level of the corresponding user account according to the comparative analysis result, and mark the user account corresponding to the corresponding historical account with the obtained active level;

[0049] Obtain corresponding resource data information, perform feature analysis on the resource data information, and obtain resource feature data of the corresponding resource data information according to the feature analysis; the information processing module is provided with resource representation feature data according to the historical resource data information in the corresponding enterprise account; and the obtained resource feature data and the resource representation feature data are subjected to similarity analysis to obtain corresponding similarity data; the process of obtaining similarity data includes:

[0050] Where XS is similarity data, X is marked as resource representation feature data, and Y is marked as resource feature data. The two are analyzed and calculated according to the Pearson correlation coefficient algorithm to obtain the corresponding similarity data;

[0051] According to the marking results of the user account and the similarity data of the resource data information, corresponding weight factors are set respectively, and the marking results and the similarity data are analyzed respectively according to the weight factors to obtain their reliability data; the reliability classification interval is preset, and the obtained reliability data is compared and analyzed with the reliability classification interval to obtain its classification result, wherein the classification result includes three types: high reliability, medium reliability and low reliability.

[0052] The data monitoring module is used to perform simulation tests on resource data information according to their classification tags, and obtain simulation monitoring data according to the simulation test results. The process includes:

[0053] The data monitoring module obtains resource data information classified and marked as medium reliability and low reliability, and sets corresponding simulation monitoring cycles according to medium reliability and low reliability respectively;

[0054] Obtain historical resource data information in the corresponding enterprise account, scramble the historical resource data information and extract lost values ​​to obtain lost resource data information, construct a virtual disguised enterprise model based on the obtained lost resource data information based on a machine learning algorithm, input the obtained resource data information into the virtual disguised enterprise model, and obtain simulated monitoring data in the virtual disguised enterprise model corresponding to the resource data information in the corresponding simulated monitoring period;

[0055] Send the simulated monitoring data corresponding to the corresponding resource data information within its corresponding simulated monitoring period to the monitoring and early warning module;

[0056] It should be further explained that, in the specific implementation process, a corresponding enterprise simulation network is set up in the corresponding enterprise account, and the enterprise simulation network includes multiple simulation monitoring nodes. The simulation monitoring nodes are interconnected with the corresponding virtual disguised enterprise models. Multiple simulation monitoring nodes can perform simulation tests on the corresponding resource data information at the same time, thereby improving the monitoring efficiency of the corresponding enterprise account.

[0057] The monitoring and early warning module is used to verify the reliability of the corresponding resource data information according to the simulated monitoring data, and determine whether the resource data information is qualified; and to perform early warning processing on the data information according to the verification result, and the process includes:

[0058] The monitoring and early warning module obtains the simulated monitoring data corresponding to the resource data information within the corresponding simulated monitoring period, performs risk assessment on the obtained simulated monitoring data, and obtains abnormal data information corresponding to the simulated monitoring data, wherein the abnormal data information includes but is not limited to outlier abnormal data, associated abnormal data and missing value abnormal data;

[0059] The outlier abnormal data refers to certain values ​​in the corresponding resource data information that are significantly different from other values, which may indicate data entry errors, system failures or fraudulent behavior;

[0060] The abnormal correlation data refers to the existence of correlation relationships between normally unrelated data in the corresponding resource data information, which may indicate data leakage, internal fraud or collusion;

[0061] The missing value abnormal data refers to the situation where a large amount of data is missing in the virtual disguised enterprise model or the pattern of missing data does not match the original situation, which may indicate system failure, attack behavior or data tampering;

[0062] The monitoring and early warning module analyzes and processes the abnormal data information of the simulated detection data within the simulated detection cycle, obtains the abnormal unit time of the abnormal data information within the corresponding simulated detection cycle, superimposes the abnormal unit time corresponding to different types of abnormal data information, obtains the abnormal risk data of the resource data information, and compares and analyzes the obtained abnormal risk data with the corresponding abnormal risk threshold according to different preset abnormal risk thresholds of the simulated detection cycle. The reliability of the corresponding resource data information is judged according to the comparative analysis results. When the abnormal risk data is greater than or equal to the corresponding abnormal risk threshold, the resource data information is unqualified and resource early warning information is generated; when the abnormal risk data is less than the corresponding abnormal risk threshold, the resource data information is qualified and sent to the information encryption module.

[0063] The information encryption module is used to encrypt the data information with qualified reliability according to the classification result of the reliability analysis corresponding to the data information, and send the encrypted data information to the information storage module. The process includes:

[0064] The information encryption module obtains qualified resource data information and user data information associated therewith, and sets corresponding key information according to the obtained user data information and resource data information;

[0065] Setting corresponding encoding strings for the user data information and resource data information obtained in the corresponding enterprise account, cross-scrambling the encoding strings corresponding to the corresponding user data information and resource data information according to the number of bits they are in, and obtaining scrambled encoding strings;

[0066] The information encryption module is provided with a coding number extraction algorithm, based on which corresponding characters in a scrambled coding string are obtained. After one extraction is completed, the scrambled coding string is digitally rearranged to obtain a new scrambled coding string, and parameters of the coding number extraction algorithm are adjusted according to the extracted characters. Based on the coding number extraction algorithm after the parameter adjustment, corresponding characters in the new scrambled coding string are obtained, and the operation is repeated until characters of corresponding digits are extracted, and the obtained characters are set as key information, and corresponding resource data information is encrypted according to the obtained key information, and the encrypted resource data information is sent to the information storage module.

[0067] The information storage module generates a corresponding resource storage space according to the corresponding enterprise account, marks the resource storage space according to the corresponding enterprise account, and stores the encrypted resource data information in the corresponding enterprise account and its corresponding user data information in the corresponding resource storage space.

[0068] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An information security management and monitoring system based on big data, including an information management center, characterized in that: The information management center is communicatively connected with an information collection module, an information processing module, a data monitoring module, a monitoring and early warning module, an information encryption module and an information storage module; The information collection module is used to collect user data information and resource data information in the corresponding enterprise account, and mark the data source of the obtained resource data information. The process includes: The information collection module is used for the user account in the enterprise account to enter the resource data information. Before entering the resource data information, the user data information of the corresponding user account in the enterprise account is entered, the obtained user data information is associated with the entered resource data information, and the obtained data information is marked according to the association result, and sent to the information processing module; The information processing module is used to perform reliability analysis on the resource data information according to its data source, and classify and mark the obtained resource data information according to the reliability analysis result. The process includes: The obtained user data information is used as the data source of the resource data information, the user data information is analyzed and processed, the historical user data information in the corresponding enterprise account stored in the information storage module is obtained, the obtained user data information is matched with the historical user data information, if the match is successful, it is marked as a historical account, if the match is not successful, it is marked as an emerging account; preset the user activity cycle, map the user data information corresponding to the successfully matched historical account and the historical user data information to the user activity cycle, obtain the activity rate of the user account, obtain the activity level of the corresponding user account according to the activity rate, preset the activity ratio level interval, compare and analyze the obtained activity rate and activity ratio level interval, and obtain the activity level of the corresponding user account according to the comparison and analysis result, wherein the activity level includes a low activity level, a normal activity level and a high activity level; Acquire corresponding resource data information, perform feature analysis on the resource data information, and acquire resource feature data of the corresponding resource data information according to the feature analysis, wherein the information processing module is provided with resource representation feature data according to the historical resource data information in the corresponding enterprise account, and perform similarity analysis on the obtained resource feature data and the resource representation feature data to acquire corresponding similarity data; The information processing module sets corresponding weight factors according to the marking results of the user account and the similarity data of the resource data information, analyzes the marking results and the similarity data according to the weight factors, and obtains the reliability data; presets the reliability classification interval, compares and analyzes the obtained reliability data with the reliability classification interval, and obtains the classification result, wherein the classification result includes three types: high reliability, medium reliability and low reliability. The data monitoring module is used to perform simulation tests on resource data information according to their classification labels, and obtain simulation monitoring data according to the simulation test results; The monitoring and early warning module is used to verify the reliability of the corresponding resource data information according to the simulated monitoring data, determine whether the resource data information is qualified, and perform early warning processing on the data information according to the judgment result; The information encryption module is used to encrypt resource data information with qualified reliability according to the classification results of the reliability analysis corresponding to the data information, and send the resource data information after the encryption processing to the information storage module; The information storage module is used to store the resource data information that has completed encryption processing according to the enterprise account.

2. According to the big data-based information security management and monitoring system of claim 1, it is characterized in that: The data monitoring module performs simulation tests on resource data information according to its classification tags, and the process of obtaining simulation monitoring data according to the simulation test results includes: The data monitoring module obtains resource data information classified as high reliability, medium reliability and low reliability, and sets corresponding simulation monitoring cycles according to the reliability levels; Obtain historical resource data information in the corresponding enterprise account, scramble the historical resource data information and extract lost values ​​to obtain lost resource data information, construct a virtual disguised enterprise model based on the obtained lost resource data information based on a machine learning algorithm, input the obtained resource data information into the virtual disguised enterprise model, and obtain simulated monitoring data in the virtual disguised enterprise model corresponding to the resource data information in the corresponding simulated monitoring period; The simulated monitoring data corresponding to the corresponding resource data information within its corresponding simulated monitoring period is sent to the monitoring and early warning module.

3. According to claim 2, the information security management and monitoring system based on big data is characterized in that: The monitoring and early warning module verifies the reliability of the corresponding resource data information according to the simulated monitoring data, and the process of judging whether the resource data information is qualified includes: The monitoring and early warning module obtains the simulated monitoring data corresponding to the resource data information within the corresponding simulated monitoring period, performs risk assessment on the obtained simulated monitoring data, and obtains abnormal data information corresponding to the simulated monitoring data, wherein the abnormal data information includes outlier abnormal data, associated abnormal data and missing value abnormal data; The monitoring and early warning module analyzes and processes the abnormal data information of the simulated detection data within the simulated detection cycle, obtains the abnormal unit time of the abnormal data information within the corresponding simulated detection cycle, superimposes the abnormal unit time corresponding to different types of abnormal data information, obtains the abnormal risk data of the resource data information, and compares and analyzes the obtained abnormal risk data with the corresponding abnormal risk threshold according to different preset abnormal risk thresholds of the simulated detection cycle. The reliability of the corresponding resource data information is judged according to the comparative analysis results. When the abnormal risk data is greater than or equal to the corresponding abnormal risk threshold, the resource data information is unqualified and resource early warning information is generated; when the abnormal risk data is less than the corresponding abnormal risk threshold, the resource data information is qualified and sent to the information encryption module.

4. The information security management and monitoring system based on big data according to claim 3 is characterized in that: The process of the information encryption module encrypting the data information according to the classification result of the reliability analysis corresponding to the data information includes: The information encryption module obtains qualified resource data information and user data information associated therewith, and sets corresponding key information according to the obtained user data information and resource data information; Setting corresponding encoding strings for the user data information and resource data information obtained in the corresponding enterprise account, cross-scrambling the encoding strings corresponding to the corresponding user data information and resource data information according to the number of bits they are in, and obtaining scrambled encoding strings; The information encryption module is provided with a coding number extraction algorithm, based on which corresponding characters in a scrambled coding string are obtained. After one extraction is completed, the scrambled coding string is digitally rearranged to obtain a new scrambled coding string, and parameters of the coding number extraction algorithm are adjusted according to the extracted characters. Based on the coding number extraction algorithm after the parameter adjustment, corresponding characters in the new scrambled coding string are obtained, and the operation is repeated until characters of corresponding digits are extracted, and the obtained characters are set as key information, and corresponding resource data information is encrypted according to the obtained key information, and the encrypted resource data information is sent to the information storage module.

5. The information security management and monitoring system based on big data according to claim 4 is characterized in that: The information storage module generates a corresponding resource storage space according to the corresponding enterprise account, marks the resource storage space according to the corresponding enterprise account, and stores the encrypted resource data information in the corresponding enterprise account and its corresponding user data information in the corresponding resource storage space.

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